Page 248 - India Insurance Report 2023- BIMTECH

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Globally there are many such tools as FEWS NET in Africa and Caribbean, MARS in Europe,
Cropland Data Layer in USA, AMIS Crop Monitor of GEOGLAM and CropWatch of China. Taranis,
FieldIn, Agritask in Israel. With some modifications, such tools can be tailored to meet the requirements
of estimating probable crop yield losses at a disaggregated scale for the crop insurance sector.

Satellite weather data is now easily available. Though there are some issues about its accuracy and
the scale for final claim payment, it is of sufficient accuracy, once corrected for bias with in situ data, to
make a preliminary estimate of crop losses during the crop season. This could be used mid-season to
make initial 25-50 percent of the claim payment to the insured pending his full settlement based on a
yield index after the harvest of the crop. Such a double trigger product would help the insured farmer in
time to address his agrarian distress.

5. Conclusion : Way forward for systems integrations and Technology Disruptions

Aside from all the system integrations, and technology disruptions already discussed, an independent
tool that uses seasonal and short-term weather forecasts, historical databases, satellite images, and current
weather data can be easily developed to provide a double trigger product for mid-season claim calculation
and ‘on-account’ payment to the insured as well as a yield index for final loss assessment and claim
settlement. The Ministry of Agriculture and Farmers’ Welfare should explore expanding the scope of its
PMFBY program for this purpose. The growing demand of technology disruptions require wide scale
adoption of block ad open-source technologies with usage of proprietary algos wherever necessary to
maintain security mechanism as the data is migrated across the network and the sharing, replication is
enabled near real time.

As the focus is to build integrated technology stack leveraging data streams and batches such as RS/
GIS/Drones /UAVs, IOTs and other socio-economic platforms, the system is required to facilitate near
real time data sharing across stakeholders With functionalities across advanced data processing and analyzing
capabilities and software tools Advanced retrieval techniques / Algos , Multi-dimensional data modeling ,
Web Analytics , IOT etc Dashboards for individual sectors, mobile data collection – dept. wise, Administrative
– Departmental and Portal management- User trails. Base map options like Satellite image, GIS base map
(Bo. Big Data Analysis - Distributed and Parallel computing for Voluminous / Variety and Velocity
(continuously updated) data undaries, Major Roads, Rivers, Landmarks), GIS themes - Forest cover,
Topography, Satellite imagery etc., ESRI Base , Open Street Map, Googl, Bing, Bhuvan etc, Map tools for
visualization of 2D, Search Engine for Portal, Metadata exploration. Data Extraction & Downloads, Spatial
queries, both Simple and Buffer based, Geo-linking of external data. Geo- Analytics. Spatial Planning and
Distribution Analysis – Predictive modeling / Trend Analysis, Geo-statistics, Map tools for 3D visualization,
Real time data handling (including Tracking Analysis, Real time timestamping), Development of web
services for data extraction and data ingestion, Configurable URL’s for web / mobile application development,
Offline GIS data visualization for mobile application, Offline GIS data analysis on desktop by various
departments. Versioning of data and data management for continuous online editing. Network analysis.

Scholarly work referred
EnhancingTechnology-Use-in-AgricultureInsurance-30-07-21.pdf (niti.gov.in)